Papers › ProvDeploy: Provenance-oriented Containerization of High Performance Computing...
ProvDeploy: Provenance-oriented Containerization of High Performance Computing Scientific Workflows
Liliane Kunstmann, Débora Pina, Daniel de Oliveira, Marta Mattoso
The archive published only this paper's code-link row. Authors, date and abstract are from arXiv's metadata (CC0), read from the Kaggle arXiv metadata snapshot of 2026-09-12 where its title matched the archive's; the title is the archive's.
Many existing scientific workflows require High Performance Computing environments to produce results in a timely manner. These workflows have several software library components and use different environments, making the deployment and execution of the software stack not trivial. This complexity increases if the user needs to add provenance data capture services to the workflow. This manuscript introduces ProvDeploy to assist the user in configuring containers for scientific workflows with integrated provenance data capture. ProvDeploy was evaluated with a Scientific Machine Learning workflow, exploring containerization strategies focused on provenance in two distinct HPC environments
Code
Repository list and official/mentioned flags are the archive's, frozen 2025-07-28. Reachability, where shown, is from one Syntology probe window (2026-09-16 to 2026-09-18); repositories not probed show nothing. GitHub stars are not tracked.
Code Syntology ran Syntology
Not run by Syntology. Nothing on this page verifies that the listed code works.
Results from the paper archive 2025-07-28
No leaderboard rows for this paper in the archive.
Report a problem or propose a change · a person checks every report against the paper or source before anything changes; decisions are listed on /corrections